HPV detection patterns in young women from the PAPCLEAR longitudinal study: implications for HPV screening policies
Bibliographic record
Abstract
Abstract Objectives HPV infections are ubiquitous. For most infections, we lose track of the presence of the virus in host in less than three years after the start of infection. The mechanisms regulating the persistence of HPV infection are still partially understood. In this work, we focus on incident HPV detection in young women and we characterise the dynamics of these infections and evaluate the effect of genotype and host socio-economic factors on the duration of HPV detection and time between detection. Methods We investigated human papillomavirus (HPV) genotype detection patterns in 182 young women in Montpellier, France. We relied on SPF 10 -LiPA25 screening assay for the simultaneous de-tection of 25 HPV genotypes. We used survival analysis tools with frailty effects to investigate the contribution of viral and host factors to variations in the time of HPV detectability, time of first incident detection, and time before re-detection. Results Women of the PAPCLEAR cohort experienced numerous positive HPV events, including frequent redetection of the same genotype. We retrieve classical results that HR-genotypes are detected for longer duration than LR-genotypes. HR-genotypes were also more liekly to be detected than LR-genotypes during the follow-up. The number of lifetime sexual partner was strongly associated with increased risk of new positive detection while vaccination was related to a lower risk of displaying incident infections. Covariates related to socio-economic difficulties were associated with longer duration of HPV positivity. Conclusions Young women display numerous event of HPV detection, with frequent codetections of multiple genotypes at the same time and redetection of the same type after periods of no detection. These new detection are almost certainly the result of new acquisition from sexual partners, with little evidence of re-emergence of latent infections. A better characterisation of transient infections might help unveil doubts and misconception on HPV physiopathology, favouring adherence to preventive policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".